Hari Mahajan — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · KI / ML · August 2026

68/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a compact rolling-bearing fault detector end to end: loaded and interpreted vibration snapshots, selected kurtosis as the strongest impulsive-fault feature, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with reported catch-rate and false-alarm rate. Also wrote a maintenance memo that defended a 0.35 alarm threshold using explicit cost trade-offs and a plain-language explanation for non-experts.

Bewertet nach

Feature engineering (signal to honest numbers)25%
Held-out evaluation (honest measurement)25%
Cost-based threshold decision + memo25%
Compact model + embedded-budget justification15%
Plain-language explanation10%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Chose kurtosis as the most responsive time-domain feature for impulsive bearing faults
Trained a depth-1 decision tree and explicitly stated depth, node count, and embedded-budget fit
Used held-out catch-rate and false-alarm rate in the threshold memo with concrete euro costs
Leakage-Free Model Training
Honest Held-Out Evaluation
Cost-Based Thresholding

Meine eingereichte Arbeit19 Aufgaben

11 Aufgaben · 6.9k Zeichen · Python · 8 Textantworten · 478 Wörter

Initial Prediction62 Zeichen
Example Load Plot HealthyPython · 952 Zeichen
Load Plot Signal CodePython · 1.8k Zeichen
Load Plot Signal205 Zeichen
Example Rms WorkedPython · 697 Zeichen
Time Features143 Zeichen
Freq FeaturesPython · 991 Zeichen
Train Classifier363 Zeichen
Evaluation Heldout288 Zeichen
Threshold Decision335 Zeichen
Decision Memo1.1k Zeichen
Initial Prediction9 Wörter
Example Load Plot Healthy90 Wörter
Load Plot Signal Code163 Wörter
Load Plot Signal15 Wörter
Example Rms Worked72 Wörter
Time Features10 Wörter
Freq Features88 Wörter
Train Classifier31 Wörter

Bewusst zusammengefasst — der eingereichte Code und die Texte bleiben privat, damit diese Seite nicht als Lösungsvorlage dient. Die vollständige Einreichung steht hinter dem verifizierten Zertifikat.

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive Safety

Perception Validation Engineer (ADAS Safety Sign-off) · KI / ML · August 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a perception validation dossier end to end for a synthetic Aurora-7 detector: defined detections with an IoU>=0.5 match rule, computed precision/recall plus the VRU false-negative rate, and sliced recall by lighting, visibility, and rarity to expose the night/occluded pedestrian risk. The final safety case tied those numbers to ISO 21448-style functional insufficiencies and produced an evidence-based ship verdict grounded in the student’s own metrics and the Tempe crash analysis.

Bewertet nach

Detection metrics correctness (precision, recall, VRU false-negative rate)30%
Failure-mode analysis & per-condition rigour25%
SOTIF safety case quality (ISO 21448 reasoning)25%
Ship verdict — evidence-based & internally consistent20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Locked the detection definition to IoU >= 0.5 and correctly treated unmatched real objects as false negatives.
Computed and carried forward the key safety metric: VRU false-negative rate, not just headline precision/recall.
Exposed the dangerous night and occluded VRU slices, with recall collapsing to 0.39 at night and 0.27 in the worst combined slice.
Wrote concrete mitigations such as daylight/ODD restriction, sensor-fusion fallback, and targeted night/rare-class data collection.
Leakage-Free Metric Definition
Safety-Critical Slice Analysis

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 14.9k Zeichen · Python · 8 Textantworten · 845 Wörter

Sensor Match553 Zeichen
Example Iou Demo1.5k Zeichen
Task Definition377 Zeichen
Task Definition Code1.1k Zeichen
Example Metrics DemoPython · 2.2k Zeichen
Scored Metrics RunPython · 2.3k Zeichen
Scored Metrics351 Zeichen
Ttcs Example SlicePython · 1.3k Zeichen
Ttcs Triage SlicePython · 1.6k Zeichen
Sotif Case2.1k Zeichen
Realcase Analysis841 Zeichen
Teachback697 Zeichen
Sensor Match27 Wörter
Example Iou Demo169 Wörter
Task Definition42 Wörter
Task Definition Code149 Wörter
Example Metrics Demo154 Wörter
Scored Metrics Run143 Wörter
Scored Metrics18 Wörter
Ttcs Example Slice143 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Softwareentwicklung · August 2026

96/ 100

Basierend auf einer öffentlichen Fallstudie von IBM · nicht mit IBM verbunden

Built and verified a full legacy-service repair for KM-Wächter: fixed the wear calculation, handled missing service readings safely, corrected the nightly fleet report, added the missing regression test, and modernized the code style. Also produced a data-driven breakdown-risk analysis that followed the fleet_history.csv evidence rather than the misleading mileage assumption, and validated the result with pytest and the project acceptance script.

Bewertet nach

Fix and extend the repo with the agent35%
Diagnose the bug20%
Direct and audit the AI agent25%
Explain it like an engineer20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly identified the integer-division bug and explained why 14,900/15,000 was being floored to 0%
Caught the agent’s planted mistakes: the reintroduced rounding, the threshold change, and the missing proving test
Wrote NOTES.md with concrete verification steps and a data-based explanation of the breakdown-risk factors
Explained the fix in plain language, including the operational risk to Fleet Ops and how the work was verified
Leakage-Free Feature Selection
Honest Acceptance Checking

Das Ergebnis

Zum Codemrcrackhari / km-waechter-fix

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 2.4k Zeichen · 8 Textantworten · 70 Wörter

Bob Trial Click136 Zeichen
Bob Ready62 Zeichen
Diagnose Line130 Zeichen
Diagnose Why119 Zeichen
Scope Prompt66 Zeichen
Audit Findings158 Zeichen
Audit Why140 Zeichen
Acceptance Checklist99 Zeichen
Template Repo Click82 Zeichen
Exec Pitch1.1k Zeichen
Bob Bonus224 Zeichen
Repo URL46 Zeichen
Bob Trial Click1 Wort
Bob Ready1 Wort
Diagnose Line10 Wörter
Diagnose Why18 Wörter
Scope Prompt1 Wort
Audit Findings1 Wort
Audit Why21 Wörter
Acceptance Checklist17 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV